Predicting Visitors Using Location-Based Social Networks

Muhammad Aamir Saleem, Felipe Soares Da Costa, Peter Dolog, Panagiotis Karras, Torben Bach Pedersen, Toon Calders

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

3 Citations (Scopus)

Abstract

Location-based social networks (LBSN) are social networks complemented with users’ location data, such as geotagged activity data. Predicting such activities finds application in marketing, recommendation systems, and logistics management. In this paper, we exploit LBSN data to predict future visitors at given locations. We fetch the travel history of visitors by their check-ins in LBSNs and identify five features that significantly drive the mobility of a visitor towards a location: (i) historic visits, (ii) location category, (iii) time, (iv) distance, and (v) friends’ activities. We provide a visitor prediction model, CMViP, based on collective matrix factorization and influence propagation. CMViP first utilizes collective matrix factorization to map the first four features to a common latent space to find visitors having a significant potential to visit a given location. Then, it utilizes an influence-mining approach to further incorporate friends of those visitors, who are influenced by the visitors’ activities and likely to follow them. Our experiments on two real-world datasets show that our methods outperform the state of art in terms of precision and accuracy.
Original languageEnglish
Title of host publication19th IEEE International Conference on Mobile Data Management (MDM)
Number of pages6
Volume2018-June
PublisherIEEE Computer Society Press
Publication date28 Jun 2018
Pages245-250
ISBN (Print)978-1-5386-4134-7
ISBN (Electronic)978-1-5386-4133-0
DOIs
Publication statusPublished - 28 Jun 2018
EventIEEE International Conference on Mobile Data Management - Comwell Hvide Hus Aalborg, Aalborg, Denmark
Duration: 26 Jun 201828 Jun 2018
Conference number: 19
http://mdmconferences.org/mdm2018/index.html

Conference

ConferenceIEEE International Conference on Mobile Data Management
Number19
LocationComwell Hvide Hus Aalborg
CountryDenmark
CityAalborg
Period26/06/201828/06/2018
Internet address
SeriesIEEE International Conference on Mobile Data Management (MDM)
ISSN2375-0324

Fingerprint

Factorization
Recommender systems
Logistics
Marketing
Experiments

Keywords

  • Collective matrix factorization
  • Influence propagation
  • Location based Social Networks
  • Visitor prediction

Cite this

Saleem, M. A., Da Costa, F. S., Dolog, P., Karras, P., Pedersen, T. B., & Calders, T. (2018). Predicting Visitors Using Location-Based Social Networks. In 19th IEEE International Conference on Mobile Data Management (MDM) (Vol. 2018-June, pp. 245-250). IEEE Computer Society Press. IEEE International Conference on Mobile Data Management (MDM) https://doi.org/10.1109/MDM.2018.00043
Saleem, Muhammad Aamir ; Da Costa, Felipe Soares ; Dolog, Peter ; Karras, Panagiotis ; Pedersen, Torben Bach ; Calders, Toon. / Predicting Visitors Using Location-Based Social Networks. 19th IEEE International Conference on Mobile Data Management (MDM). Vol. 2018-June IEEE Computer Society Press, 2018. pp. 245-250 (IEEE International Conference on Mobile Data Management (MDM)).
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abstract = "Location-based social networks (LBSN) are social networks complemented with users’ location data, such as geotagged activity data. Predicting such activities finds application in marketing, recommendation systems, and logistics management. In this paper, we exploit LBSN data to predict future visitors at given locations. We fetch the travel history of visitors by their check-ins in LBSNs and identify five features that significantly drive the mobility of a visitor towards a location: (i) historic visits, (ii) location category, (iii) time, (iv) distance, and (v) friends’ activities. We provide a visitor prediction model, CMViP, based on collective matrix factorization and influence propagation. CMViP first utilizes collective matrix factorization to map the first four features to a common latent space to find visitors having a significant potential to visit a given location. Then, it utilizes an influence-mining approach to further incorporate friends of those visitors, who are influenced by the visitors’ activities and likely to follow them. Our experiments on two real-world datasets show that our methods outperform the state of art in terms of precision and accuracy.",
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Saleem, MA, Da Costa, FS, Dolog, P, Karras, P, Pedersen, TB & Calders, T 2018, Predicting Visitors Using Location-Based Social Networks. in 19th IEEE International Conference on Mobile Data Management (MDM). vol. 2018-June, IEEE Computer Society Press, IEEE International Conference on Mobile Data Management (MDM), pp. 245-250, IEEE International Conference on Mobile Data Management, Aalborg, Denmark, 26/06/2018. https://doi.org/10.1109/MDM.2018.00043

Predicting Visitors Using Location-Based Social Networks. / Saleem, Muhammad Aamir; Da Costa, Felipe Soares; Dolog, Peter; Karras, Panagiotis; Pedersen, Torben Bach; Calders, Toon.

19th IEEE International Conference on Mobile Data Management (MDM). Vol. 2018-June IEEE Computer Society Press, 2018. p. 245-250 (IEEE International Conference on Mobile Data Management (MDM)).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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AU - Calders, Toon

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Saleem MA, Da Costa FS, Dolog P, Karras P, Pedersen TB, Calders T. Predicting Visitors Using Location-Based Social Networks. In 19th IEEE International Conference on Mobile Data Management (MDM). Vol. 2018-June. IEEE Computer Society Press. 2018. p. 245-250. (IEEE International Conference on Mobile Data Management (MDM)). https://doi.org/10.1109/MDM.2018.00043